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<Story> (title to be decided)#

	Area	Item	Element	Unit	2000	2001	2002	2003	2004	2005	...	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020
0	Afghanistan	Crop Residues	Direct emissions (N2O)	kilotonnes	0.520	0.5267	0.8200	0.9988	0.8225	1.1821	...	1.0321	1.3726	1.4018	1.4584	1.2424	1.1940	1.0617	0.8988	1.2176	1.3170
1	Afghanistan	Crop Residues	Indirect emissions (N2O)	kilotonnes	0.117	0.1185	0.1845	0.2247	0.1851	0.2660	...	0.2322	0.3088	0.3154	0.3281	0.2795	0.2687	0.2389	0.2022	0.2740	0.2963
2	Afghanistan	Crop Residues	Emissions (N2O)	kilotonnes	0.637	0.6452	1.0045	1.2235	1.0075	1.4481	...	1.2643	1.6815	1.7173	1.7865	1.5220	1.4627	1.3005	1.1011	1.4916	1.6133
3	Afghanistan	Crop Residues	Emissions (CO2eq) from N2O (AR5)	kilotonnes	168.807	170.9884	266.1975	324.2195	266.9995	383.7498	...	335.0379	445.5958	455.0727	473.4174	403.3181	387.6130	344.6447	291.7838	395.2689	427.5284
4	Afghanistan	Crop Residues	Emissions (CO2eq) (AR5)	kilotonnes	168.807	170.9884	266.1975	324.2195	266.9995	383.7498	...	335.0379	445.5958	455.0727	473.4174	403.3181	387.6130	344.6447	291.7838	395.2689	427.5284

5 rows × 25 columns

Element
Emissions (CO2eq) (AR5)                 11926
Emissions (N2O)                          9622
Emissions (CO2eq) from N2O (AR5)         9486
Emissions (CH4)                          8310
Emissions (CO2eq) from CH4 (AR5)         8167
Emissions (CO2)                          6677
Direct emissions (N2O)                   1912
Indirect emissions (N2O)                 1756
Emissions (CO2eq) from F-gases (AR5)      909
Name: count, dtype: int64

The First Perspective#

The different kinds of sustainable energy per country affect the emissions in that country so that there are less emissions. It wins back renewable energy in a sustainable way so that there is less need to obtain energy causing bad emissions. So there will be a positive correlation between sustainable energy and lessening the emissions per country.

The First Argument of Your First Perspective#

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
Cell In[7], line 3
years = ['2000', '2005', '2010', '2015', '2020']
----> 3 mergeN = pd.read_csv('../datasets/graphMakers/graphA/mergeN.csv')
"""
# Calculate the average of the relative values for each country
mergeN['avg'] = mergeN[[year for year in years]].mean(axis=1)
   (...)
fig.show()
"""
# Calculate the average of the relative values for each country

File ~/miniconda3/envs/jupyterbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1026, in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)
kwds_defaults = _refine_defaults_read(
   dialect,
   delimiter,
   (...)
   dtype_backend=dtype_backend,
)
kwds.update(kwds_defaults)
-> 1026 return _read(filepath_or_buffer, kwds)

File ~/miniconda3/envs/jupyterbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:620, in _read(filepath_or_buffer, kwds)
_validate_names(kwds.get("names", None))
# Create the parser.
--> 620 parser = TextFileReader(filepath_or_buffer, **kwds)
if chunksize or iterator:
   return parser

File ~/miniconda3/envs/jupyterbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1620, in TextFileReader.__init__(self, f, engine, **kwds)
   self.options["has_index_names"] = kwds["has_index_names"]
self.handles: IOHandles | None = None
-> 1620 self._engine = self._make_engine(f, self.engine)

File ~/miniconda3/envs/jupyterbook/lib/python3.10/site-packages/pandas/io/parsers/readers.py:1880, in TextFileReader._make_engine(self, f, engine)
   if "b" not in mode:
       mode += "b"
-> 1880 self.handles = get_handle(
   f,
   mode,
   encoding=self.options.get("encoding", None),
   compression=self.options.get("compression", None),
   memory_map=self.options.get("memory_map", False),
   is_text=is_text,
   errors=self.options.get("encoding_errors", "strict"),
   storage_options=self.options.get("storage_options", None),
)
assert self.handles is not None
f = self.handles.handle

File ~/miniconda3/envs/jupyterbook/lib/python3.10/site-packages/pandas/io/common.py:873, in get_handle(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)
elif isinstance(handle, str):
   # Check whether the filename is to be opened in binary mode.
   # Binary mode does not support 'encoding' and 'newline'.
   if ioargs.encoding and "b" not in ioargs.mode:
       # Encoding
--> 873         handle = open(
           handle,
           ioargs.mode,
           encoding=ioargs.encoding,
           errors=errors,
           newline="",
       )
   else:
       # Binary mode
       handle = open(handle, ioargs.mode)

FileNotFoundError: [Errno 2] No such file or directory: '../datasets/graphMakers/graphA/mergeN.csv'

Figure 1: Shows emissions of the best and worst countries.

Figure 2: Electricity obtained from renewables, nuclear and fossil fuels for countries with the least and most emissions in 2020.

Figure 2 shows countries with the least amount of emissions do gain some of their electricity through renewable sources. This could mean the sustainability of it has a positive effect on lowering the emissions. They also have less electricity from fossil fuels than the countries with most emissions.

Even though two of the countries that have most emissions have a relatively high amount of electricity through renewables and nuclear sources, the emissions can be explained because of the high amount of electricity gained from fossil fuels which increase the emissions. The renewables could still help out to lower the emissions but is being overshadowed by the large amount of electricity from fossil fuels. This theory came to life when comparing Singapore from figure 1 with figure 2, because Singapore has the most emissions taking in consideration the land area in figure 1 and it shows to have a lot of electricity gained from fossil fuels in figure 2.

The Second Argument of Your First Perspective#

Figure 2: Countries of the world, colored by CO2 emissions from 2020.

As you can see, most small countries are in the red, which means that their CO2 emissions per square kilometer are very high.

Your Second Perspective#

The First Argument of Your Second Perspective#

If you look figure 2, two of these countries get loads of energy from renewable sources, yet they are still in the top 10 most emitting countries.

Summary#

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